Belief Function Definition for Ensemble Methods - Application to Pedestrian Detection in Dense Crowds

—Large scale social events are characterized by very high densities (at least locally) and an increased risk of congestions and fatal accidents. Our work focuses on the specific problem of pedestrian detection in high-density crowd images, denoted by strong homogeneity and clutter. We propose and compare different evidential fusion algorithms which are able to exploit multiple detectors based on different gradient, texture and orientation descriptors. The evidential framework allows us to model spatial imprecision arising from each of the detectors, both in the calibration and in the spatial domains. Moreover, we propose a bba allocation that takes into account both types of imprecision. Results on difficult high-density crowd images acquired at Makkah during the Muslim pilgrimage show that the proposed combined fusion algorithm leads to better results than taking into account only individual sources of imprecision.